Sümbül Uygar, Song Sen, McCulloch Kyle, Becker Michael, Lin Bin, Sanes Joshua R, Masland Richard H, Seung H Sebastian
1] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA [2] Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts 02114, USA [3].
1] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA [2] Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China [3].
Nat Commun. 2014 Mar 24;5:3512. doi: 10.1038/ncomms4512.
The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determine the spatial distribution of the dendritic arbors, or arbor density, with reference to arbors of an abundant, well-defined interneuronal type. The arbor densities are sorted into a number of clusters that is set by comparison with several molecularly defined cell types. The algorithm reproduces the genetic classes that are pure types, and detects six newly clustered cell types that await genetic definition.
细胞类型在理解脑功能中的重要性已得到广泛认可，但只有极小一部分神经元多样性得到了分类编目。在此，我们利用细胞类型遗传定义方面的最新进展，采用一种客观的结构方法对神经元进行分类。该方法基于对树突分支相对于其他细胞神经突位置的高精度量化。我们在363个小鼠视网膜神经节细胞群体上测试了该方法。对于每个细胞，我们参照一种丰富且定义明确的中间神经元类型的树突分支来确定树突分支的空间分布或分支密度。通过与几种分子定义的细胞类型进行比较，将分支密度分类为若干个簇。该算法重现了纯类型的遗传类别，并检测到六种有待遗传定义的新聚类细胞类型。
